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Data X:
17080 9 57 0 0 17240 8 68 0 0 17200 7 55 0 0 15580 9 53 0 1 18950 9 57 0 1 23360 8 61 0 0 19190 6 58 0 0 14150 6 56 0 0 19870 8 59 0 0 19420 9 60 0 0 16020 6 53 0 0 17350 8 54 0 1 21930 8 59 0 0 21180 8 61 0 1 22580 8 58 0 1 19320 7 53 0 1 14720 5 50 0 1 18780 6 53 0 0 23520 9 59 0 1 26040 9 62 0 1 14000 5 49 0 0 12560 5 53 0 0 25780 7 63 0 1 29880 9 65 0 0 14040 3 52 0 1 23480 9 60 0 1 17550 5 52 0 1 29800 8 60 0 0 21000 9 60 0 0 12820 5 49 0 0 30000 9 66 0 1 26730 8 60 0 0 20930 7 58 0 0 16120 5 52 0 0 21750 8 59 0 0 27250 9 59 0 1 20710 8 55 0 1 15470 8 57 0 1 20040 8 57 0 1 31350 9 60 0 0 24200 8 59 0 1 17760 5 51 0 1 19310 8 57 0 0 13430 5 50 0 0 20760 9 57 0 0 16240 7 54 0 1 13440 8 53 0 0 16500 6 55 0 1 27320 8 61 0 1 20170 5 55 0 1 27970 9 62 0 0 35560 9 62 0 1 17030 8 55 0 1 16340 6 54 0 1 25700 9 57 0 1 30160 9 63 0 0 24190 7 60 0 0 15690 4 50 0 0 16980 8 58 0 0 21230 8 60 0 1 24810 8 60 0 0 14810 6 51 0 0 15770 4 49 0 0 19400 8 59 0 1 17470 6 58 0 1 20690 9 58 0 1 16310 7 56 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0 0 28030 9 60 0 1 29230 9 64 0 1 23580 8 61 0 0 20940 8 58 0 1 18550 9 60 0 1 15350 6 55 0 0 21350 7 56 0 1 19300 5 51 0 1 21820 9 60 0 0 13590 5 51 0 1 20020 7 58 0 0 16990 6 54 0 1 25000 8 57 0 1 23660 7 58 0 0 20690 8 60 0 0 14180 4 49 0 0 23330 8 57 0 0 15140 5 52 0 1 17580 8 52 0 0 25350 7 60 0 1 25640 7 58 0 0 24870 9 64 0 0 15910 9 57 0 0 16240 8 53 0 1 27980 9 62 0 1 16910 6 53 0 1 19990 8 57 0 0 18690 9 57 0 1 10040 4 48 0 1 14270 6 50 0 1 18260 7 51 0 1 26880 9 60 0 0 16570 8 56 0 1 16720 6 54 0 0 20150 8 58 0 0 23710 7 56 0 0 21150 5 50 0 1 23280 8 60 0 0 14950 7 57 0 0 28840 11 69 0 1 23280 10 64 0 1 33810 14 63 0 1 21700 11 58 0 0 34700 11 67 0 1 30580 12 61 0 0 18110 10 57 0 1 25240 11 64 0 1 26420 10 61 0 0 37410 14 69 0 1 43360 13 70 0 1 48420 14 72 0 1 45500 12 71 0 1 28410 12 63 0 0 31660 10 62 0 0 38160 13 64 0 0 25610 10 62 0 1 36540 11 65 0 0 24810 10 61 0 1 26650 11 63 0 0 32030 10 66 0 1 35490 13 68 0 1 22360 14 66 1 0 32220 11 72 0 1 31110 10 66 0 1 34900 11 67 0 0 31470 13 64 0 0 25200 10 61 0 0 22920 10 63 0 1 28890 12 64 0 0 22460 10 61 0 1 19370 10 62 0 1 26460 10 60 0 1 29570 11 65 0 1 40070 11 67 0 1 23860 11 62 0 0 32510 10 66 0 1 27620 11 60 0 0 30110 11 64 0 0 43050 13 69 0 1 39060 13 67 0 1 35830 11 67 0 1 32360 11 66 0 0 34360 14 63 0 1 30580 11 61 0 1 30070 10 62 0 1 34890 10 67 0 1 28640 10 60 0 0 34280 14 64 1 0 28190 13 62 0 0 22500 10 58 0 0 46830 14 69 0 1 23520 10 62 0 1 31080 11 65 0 1 39940 13 67 0 1 43930 12 69 0 1 32080 13 61 1 0 25920 10 65 0 1 31930 13 70 0 1 16940 11 60 1 1 39570 14 72 1 1 23460 11 59 0 0 47890 13 69 1 1 35150 11 68 0 1 27540 11 66 0 0 27200 10 66 0 1 24630 11 65 0 1 26330 11 62 0 0 30480 10 66 0 0 31110 11 68 0 1 37450 13 68 0 0 23840 12 64 1 0 20940 10 59 0 1 31830 10 66 0 0 30740 14 65 1 0 39770 11 71 0 1 33540 10 63 0 1 34110 11 64 0 0 23870 10 66 1 0 31710 11 63 0 0 38870 13 68 0 1 26460 13 62 0 0 25040 10 60 0 0 35870 11 65 0 1 38450 11 69 0 1 29710 12 65 0 1 28910 10 61 0 0 18230 10 57 0 0 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60 0 1 33430 12 68 1 1 32000 10 65 0 1 29130 12 64 0 1 48770 13 73 0 1 23580 10 59 0 0 32790 12 71 0 1 25810 10 66 0 1 23470 12 62 0 0 26910 10 67 0 0 28270 11 63 0 0 18730 10 53 0 1 37510 12 72 1 1 25380 14 71 0 0 27580 10 66 0 1 30500 10 60 0 0 30790 12 60 0 0 22010 10 61 0 1 18580 10 59 0 1 22160 13 68 1 0 34030 12 62 0 0 35010 12 65 0 0 25780 11 63 0 0 30780 13 66 1 0 31860 12 67 1 0 16650 10 57 0 1 20810 11 63 0 0 29740 11 62 0 0 32970 13 65 1 0 40730 12 69 0 1 44480 13 69 0 1 39840 13 71 0 1 22500 10 58 0 0 27520 12 64 0 0 23040 12 67 1 1 36800 14 67 0 1 31020 11 64 1 0 28620 10 61 0 0 26770 13 67 1 0 30230 11 68 0 0 36810 11 68 0 0 32550 13 67 0 0 36920 12 67 0 1 23560 10 61 0 0 45910 10 70 0 1 30820 12 64 0 0 32970 13 65 1 0 32580 11 63 0 0 22160 10 61 0 1 32470 11 66 0 1 43240 11 68 0 1 23620 11 61 0 0 25630 11 63 0 0 32060 11 64 0 1 35850 14 70 0 1 47200 12 72 0 1 33310 13 66 0 0 50830 13 74 0 1 34980 10 68 1 1 24170 12 61 0 0 23640 10 61 0 1 23410 10 61 0 1 27590 12 62 1 0 29530 11 67 1 0 32310 12 63 0 1 30780 11 68 0 1 33690 11 71 0 1 35290 12 71 0 1 28660 12 62 0 0 28910 14 62 0 0 30220 11 62 0 0 31270 10 62 0 1 28660 11 61 0 0 26050 12 63 0 0 30560 13 63 0 0 25690 12 63 0 0 25010 11 62 0 0 33200 11 66 0 1 21230 11 65 0 1 37800 14 70 0 1 38470 11 66 0 1 37850 13 63 1 0 39240 12 68 0 1 21320 10 59 0 1 27520 12 69 0 1 24490 13 63 0 0 34560 10 63 0 1 30730 10 66 0 0 26880 10 62 0 0 33290 10 68 0 1 42710 14 73 0 1 35300 12 64 0 1 29280 11 66 0 1 26890 11 62 0 0 23320 12 57 0 1 29340 14 64 0 0 22760 14 66 1 1 31100 10 65 0 1 28940 11 67 0 1 46370 11 72 1 1 24350 10 65 0 0 28380 10 63 0 0 30350 12 62 0 0 48310 12 71 0 1 28120 11 61 0 1 27140 12 66 0 0 30860 10 62 0 0 35190 12 66 0 0 42320 13 71 0 1 27700 10 62 0 1 33410 12 66 0 0 30900 10 65 0 1 25310 13 61 0 1 28220 12 70 0 1 30380 10 65 1 0 29350 12 66 0 1 25680 10 64 0 0 23870 11 61 0 1 24990 12 65 0 1 41300 11 67 0 1 30010 12 64 0 0 31320 10 60 0 0 35770 13 64 0 0 32220 12 61 0 0 32800 11 66 0 1 26590 11 64 0 1 28220 11 62 0 0 21400 11 61 0 0 42030 12 71 0 1 29970 14 65 0 0 31200 11 61 1 0 25620 11 63 0 0 30820 12 65 0 0 38060 14 68 0 1 33390 11 69 1 1 31520 13 62 1 0 24580 11 60 0 0 23910 10 60 0 1 31410 13 61 0 0 25790 12 63 0 0 31040 11 68 1 0 40450 13 69 1 1 47630 14 68 1 1 21000 10 58 0 1 30690 11 65 1 0 27850 11 69 0 1 42840 15 70 0 1 45060 15 71 1 1 29060 18 66 0 0 51020 19 72 0 1 35190 19 66 1 0 36880 16 68 1 1 44290 17 70 0 1 42790 15 68 0 1 45000 15 70 0 1 26350 15 64 0 0 26790 15 66 1 0 21980 15 62 1 0 33450 19 66 1 0 30820 18 65 0 0 33870 16 67 0 0 30820 17 67 1 1 29030 16 63 1 0 30040 15 64 1 0 57930 15 69 0 1 39850 15 71 0 1 42200 18 68 0 1 47240 17 71 0 1 37310 15 67 0 1 34060 17 69 1 1 35000 17 62 0 0 36740 16 68 0 0 56330 17 73 0 1 31220 15 64 1 0 33300 15 69 1 0 26080 16 62 1 0 36450 16 74 0 1 37990 15 67 1 1 40860 18 67 1 1 28870 15 63 0 0 40700 16 70 1 1 39600 17 70 0 1 42990 16 66 0 1 29810 16 66 0 0 22640 15 63 1 0 44040 18 71 1 1 22780 15 60 1 0 45040 16 72 0 1 56380 17 70 0 1 48720 16 72 1 1 42700 16 67 1 1 37270 15 68 1 1 28530 18 60 0 0 27950 16 63 1 0 32110 15 67 0 0
Names of X columns:
FEV Age Ht Smoker gender
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
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11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
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Summary of computational transaction
Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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